{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.models as models\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision.transforms import RandAugment\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import time\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "from NonHighwayNet import *\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    " ## 修改模型\n",
    "net = NonHighwayNet(drop = 0.2).to(device)\n",
    "num_epochs=50\n",
    " # 定义损失函数和优化器\n",
    "trainloader,testloader = get_data_loader()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/49 轮训练\n",
      "----------\n",
      "train 损失: 4.2537 Top-1 准确率: 0.0600 Top-5 准确率: 0.2010\n",
      "val 损失: 4.1273 Top-1 准确率: 0.0683 Top-5 准确率: 0.2365\n",
      "\n",
      "第 1/49 轮训练\n",
      "----------\n",
      "train 损失: 3.8707 Top-1 准确率: 0.1228 Top-5 准确率: 0.3300\n",
      "val 损失: 3.6133 Top-1 准确率: 0.1424 Top-5 准确率: 0.3836\n",
      "\n",
      "第 2/49 轮训练\n",
      "----------\n",
      "train 损失: 3.5165 Top-1 准确率: 0.1887 Top-5 准确率: 0.4308\n",
      "val 损失: 3.4168 Top-1 准确率: 0.1836 Top-5 准确率: 0.4541\n",
      "\n",
      "第 3/49 轮训练\n",
      "----------\n",
      "train 损失: 3.2141 Top-1 准确率: 0.2484 Top-5 准确率: 0.5100\n",
      "val 损失: 2.9652 Top-1 准确率: 0.2497 Top-5 准确率: 0.5538\n",
      "\n",
      "第 4/49 轮训练\n",
      "----------\n",
      "train 损失: 2.9828 Top-1 准确率: 0.2958 Top-5 准确率: 0.5634\n",
      "val 损失: 2.4644 Top-1 准确率: 0.3607 Top-5 准确率: 0.6879\n",
      "\n",
      "第 5/49 轮训练\n",
      "----------\n",
      "train 损失: 2.8085 Top-1 准确率: 0.3369 Top-5 准确率: 0.6007\n",
      "val 损失: 2.4020 Top-1 准确率: 0.3652 Top-5 准确率: 0.6926\n",
      "\n",
      "第 6/49 轮训练\n",
      "----------\n",
      "train 损失: 2.6607 Top-1 准确率: 0.3697 Top-5 准确率: 0.6304\n",
      "val 损失: 2.3289 Top-1 准确率: 0.3947 Top-5 准确率: 0.7071\n",
      "\n",
      "第 7/49 轮训练\n",
      "----------\n",
      "train 损失: 2.5394 Top-1 准确率: 0.3939 Top-5 准确率: 0.6546\n",
      "val 损失: 1.9817 Top-1 准确率: 0.4646 Top-5 准确率: 0.7785\n",
      "\n",
      "第 8/49 轮训练\n",
      "----------\n",
      "train 损失: 2.4437 Top-1 准确率: 0.4148 Top-5 准确率: 0.6736\n",
      "val 损失: 2.0082 Top-1 准确率: 0.4641 Top-5 准确率: 0.7662\n",
      "\n",
      "第 9/49 轮训练\n",
      "----------\n",
      "train 损失: 2.3590 Top-1 准确率: 0.4373 Top-5 准确率: 0.6926\n",
      "val 损失: 1.9361 Top-1 准确率: 0.4787 Top-5 准确率: 0.7869\n",
      "\n",
      "第 10/49 轮训练\n",
      "----------\n",
      "train 损失: 2.2843 Top-1 准确率: 0.4521 Top-5 准确率: 0.7098\n",
      "val 损失: 1.8857 Top-1 准确率: 0.4933 Top-5 准确率: 0.7889\n",
      "\n",
      "第 11/49 轮训练\n",
      "----------\n",
      "train 损失: 2.2214 Top-1 准确率: 0.4672 Top-5 准确率: 0.7235\n",
      "val 损失: 1.7742 Top-1 准确率: 0.5221 Top-5 准确率: 0.8121\n",
      "\n",
      "第 12/49 轮训练\n",
      "----------\n",
      "train 损失: 2.1517 Top-1 准确率: 0.4821 Top-5 准确率: 0.7360\n",
      "val 损失: 1.8279 Top-1 准确率: 0.5061 Top-5 准确率: 0.8010\n",
      "\n",
      "第 13/49 轮训练\n",
      "----------\n",
      "train 损失: 2.1047 Top-1 准确率: 0.4913 Top-5 准确率: 0.7474\n",
      "val 损失: 1.6737 Top-1 准确率: 0.5417 Top-5 准确率: 0.8261\n",
      "\n",
      "第 14/49 轮训练\n",
      "----------\n",
      "train 损失: 2.0669 Top-1 准确率: 0.5022 Top-5 准确率: 0.7601\n",
      "val 损失: 1.6188 Top-1 准确率: 0.5538 Top-5 准确率: 0.8338\n",
      "\n",
      "第 15/49 轮训练\n",
      "----------\n",
      "train 损失: 2.0156 Top-1 准确率: 0.5119 Top-5 准确率: 0.7695\n",
      "val 损失: 1.5837 Top-1 准确率: 0.5718 Top-5 准确率: 0.8432\n",
      "\n",
      "第 16/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9811 Top-1 准确率: 0.5200 Top-5 准确率: 0.7765\n",
      "val 损失: 1.6064 Top-1 准确率: 0.5578 Top-5 准确率: 0.8379\n",
      "\n",
      "第 17/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9456 Top-1 准确率: 0.5269 Top-5 准确率: 0.7850\n",
      "val 损失: 1.7466 Top-1 准确率: 0.5321 Top-5 准确率: 0.8183\n",
      "\n",
      "第 18/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9052 Top-1 准确率: 0.5389 Top-5 准确率: 0.7953\n",
      "val 损失: 1.7404 Top-1 准确率: 0.5308 Top-5 准确率: 0.8117\n",
      "\n",
      "第 19/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8595 Top-1 准确率: 0.5479 Top-5 准确率: 0.8017\n",
      "val 损失: 1.5049 Top-1 准确率: 0.5899 Top-5 准确率: 0.8500\n",
      "\n",
      "第 20/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8165 Top-1 准确率: 0.5567 Top-5 准确率: 0.8129\n",
      "val 损失: 1.5312 Top-1 准确率: 0.5757 Top-5 准确率: 0.8502\n",
      "\n",
      "第 21/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7996 Top-1 准确率: 0.5607 Top-5 准确率: 0.8147\n",
      "val 损失: 1.4834 Top-1 准确率: 0.5930 Top-5 准确率: 0.8571\n",
      "\n",
      "第 22/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7642 Top-1 准确率: 0.5697 Top-5 准确率: 0.8238\n",
      "val 损失: 1.4142 Top-1 准确率: 0.6050 Top-5 准确率: 0.8655\n",
      "\n",
      "第 23/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7375 Top-1 准确率: 0.5743 Top-5 准确率: 0.8285\n",
      "val 损失: 1.5456 Top-1 准确率: 0.5798 Top-5 准确率: 0.8491\n",
      "\n",
      "第 24/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7187 Top-1 准确率: 0.5782 Top-5 准确率: 0.8354\n",
      "val 损失: 1.3909 Top-1 准确率: 0.6173 Top-5 准确率: 0.8685\n",
      "\n",
      "第 25/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6849 Top-1 准确率: 0.5888 Top-5 准确率: 0.8397\n",
      "val 损失: 1.4524 Top-1 准确率: 0.6037 Top-5 准确率: 0.8623\n",
      "\n",
      "第 26/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6492 Top-1 准确率: 0.5949 Top-5 准确率: 0.8481\n",
      "val 损失: 1.4111 Top-1 准确率: 0.6159 Top-5 准确率: 0.8672\n",
      "\n",
      "第 27/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6353 Top-1 准确率: 0.5975 Top-5 准确率: 0.8543\n",
      "val 损失: 1.4651 Top-1 准确率: 0.6065 Top-5 准确率: 0.8593\n",
      "\n",
      "第 28/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5974 Top-1 准确率: 0.6060 Top-5 准确率: 0.8571\n",
      "val 损失: 1.2228 Top-1 准确率: 0.6601 Top-5 准确率: 0.8956\n",
      "\n",
      "第 29/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5935 Top-1 准确率: 0.6069 Top-5 准确率: 0.8623\n",
      "val 损失: 1.3486 Top-1 准确率: 0.6309 Top-5 准确率: 0.8768\n",
      "\n",
      "第 30/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5702 Top-1 准确率: 0.6130 Top-5 准确率: 0.8666\n",
      "val 损失: 1.3741 Top-1 准确率: 0.6181 Top-5 准确率: 0.8743\n",
      "\n",
      "第 31/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5590 Top-1 准确率: 0.6164 Top-5 准确率: 0.8702\n",
      "val 损失: 1.3279 Top-1 准确率: 0.6298 Top-5 准确率: 0.8799\n",
      "\n",
      "第 32/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5152 Top-1 准确率: 0.6264 Top-5 准确率: 0.8744\n",
      "val 损失: 1.3965 Top-1 准确率: 0.6216 Top-5 准确率: 0.8669\n",
      "\n",
      "第 33/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4962 Top-1 准确率: 0.6281 Top-5 准确率: 0.8805\n",
      "val 损失: 1.3153 Top-1 准确率: 0.6404 Top-5 准确率: 0.8805\n",
      "\n",
      "第 34/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4837 Top-1 准确率: 0.6314 Top-5 准确率: 0.8846\n",
      "val 损失: 1.3018 Top-1 准确率: 0.6466 Top-5 准确率: 0.8874\n",
      "\n",
      "第 35/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4680 Top-1 准确率: 0.6352 Top-5 准确率: 0.8876\n",
      "val 损失: 1.2334 Top-1 准确率: 0.6644 Top-5 准确率: 0.8954\n",
      "\n",
      "第 36/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4411 Top-1 准确率: 0.6422 Top-5 准确率: 0.8913\n",
      "val 损失: 1.2670 Top-1 准确率: 0.6495 Top-5 准确率: 0.8912\n",
      "\n",
      "第 37/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4230 Top-1 准确率: 0.6468 Top-5 准确率: 0.8964\n",
      "val 损失: 1.2748 Top-1 准确率: 0.6497 Top-5 准确率: 0.8876\n",
      "\n",
      "第 38/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3904 Top-1 准确率: 0.6554 Top-5 准确率: 0.8992\n",
      "val 损失: 1.2948 Top-1 准确率: 0.6506 Top-5 准确率: 0.8897\n",
      "\n",
      "第 39/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3888 Top-1 准确率: 0.6536 Top-5 准确率: 0.9036\n",
      "val 损失: 1.2593 Top-1 准确率: 0.6532 Top-5 准确率: 0.8955\n",
      "\n",
      "第 40/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3576 Top-1 准确率: 0.6601 Top-5 准确率: 0.9075\n",
      "val 损失: 1.2165 Top-1 准确率: 0.6706 Top-5 准确率: 0.8956\n",
      "\n",
      "第 41/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3604 Top-1 准确率: 0.6598 Top-5 准确率: 0.9084\n",
      "val 损失: 1.2991 Top-1 准确率: 0.6457 Top-5 准确率: 0.8864\n",
      "\n",
      "第 42/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3275 Top-1 准确率: 0.6677 Top-5 准确率: 0.9146\n",
      "val 损失: 1.5833 Top-1 准确率: 0.5939 Top-5 准确率: 0.8499\n",
      "\n",
      "第 43/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3199 Top-1 准确率: 0.6693 Top-5 准确率: 0.9162\n",
      "val 损失: 1.3431 Top-1 准确率: 0.6388 Top-5 准确率: 0.8846\n",
      "\n",
      "第 44/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3130 Top-1 准确率: 0.6719 Top-5 准确率: 0.9186\n",
      "val 损失: 1.2417 Top-1 准确率: 0.6627 Top-5 准确率: 0.8934\n",
      "\n",
      "第 45/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2886 Top-1 准确率: 0.6775 Top-5 准确率: 0.9229\n",
      "val 损失: 1.3525 Top-1 准确率: 0.6445 Top-5 准确率: 0.8823\n",
      "\n",
      "第 46/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2664 Top-1 准确率: 0.6828 Top-5 准确率: 0.9268\n",
      "val 损失: 1.5574 Top-1 准确率: 0.6084 Top-5 准确率: 0.8530\n",
      "\n",
      "第 47/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2636 Top-1 准确率: 0.6813 Top-5 准确率: 0.9270\n",
      "val 损失: 1.2314 Top-1 准确率: 0.6679 Top-5 准确率: 0.8987\n",
      "\n",
      "第 48/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2559 Top-1 准确率: 0.6840 Top-5 准确率: 0.9285\n",
      "val 损失: 1.3401 Top-1 准确率: 0.6480 Top-5 准确率: 0.8825\n",
      "\n",
      "第 49/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2360 Top-1 准确率: 0.6870 Top-5 准确率: 0.9323\n",
      "val 损失: 1.3151 Top-1 准确率: 0.6558 Top-5 准确率: 0.8826\n",
      "\n",
      "训练完成，耗时 46m 9s\n",
      "最佳验证集准确率: 0.670600\n"
     ]
    }
   ],
   "source": [
    "model,(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs) = train_model(net,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_cpu_numpy(tensor):\n",
    "    return tensor.cpu().numpy()\n",
    "def to_cpu(list_):\n",
    "    if isinstance(list_, list):\n",
    "        return list(map(to_cpu_numpy,list_))\n",
    "    else:\n",
    "        return to_cpu_numpy(list_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_results(train_losses, to_cpu(train_top1_accs), to_cpu(train_top5_accs), val_losses, to_cpu(val_top1_accs), to_cpu(val_top5_accs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/29 轮训练\n",
      "----------\n",
      "train 损失: 1.1767 Top-1 准确率: 0.7054 Top-5 准确率: 0.9243\n",
      "val 损失: 0.9939 Top-1 准确率: 0.7225 Top-5 准确率: 0.9233\n",
      "\n",
      "第 1/29 轮训练\n",
      "----------\n",
      "train 损失: 1.0984 Top-1 准确率: 0.7243 Top-5 准确率: 0.9394\n",
      "val 损失: 0.9583 Top-1 准确率: 0.7334 Top-5 准确率: 0.9293\n",
      "\n",
      "第 2/29 轮训练\n",
      "----------\n",
      "train 损失: 1.0922 Top-1 准确率: 0.7238 Top-5 准确率: 0.9468\n",
      "val 损失: 0.9512 Top-1 准确率: 0.7326 Top-5 准确率: 0.9275\n",
      "\n",
      "第 3/29 轮训练\n",
      "----------\n",
      "train 损失: 1.0725 Top-1 准确率: 0.7303 Top-5 准确率: 0.9488\n",
      "val 损失: 0.9662 Top-1 准确率: 0.7313 Top-5 准确率: 0.9259\n",
      "\n",
      "第 4/29 轮训练\n",
      "----------\n",
      "train 损失: 1.0407 Top-1 准确率: 0.7390 Top-5 准确率: 0.9538\n",
      "val 损失: 0.9401 Top-1 准确率: 0.7370 Top-5 准确率: 0.9304\n",
      "\n",
      "第 5/29 轮训练\n",
      "----------\n",
      "train 损失: 1.0399 Top-1 准确率: 0.7381 Top-5 准确率: 0.9561\n",
      "val 损失: 0.9370 Top-1 准确率: 0.7376 Top-5 准确率: 0.9309\n",
      "\n",
      "第 6/29 轮训练\n",
      "----------\n",
      "train 损失: 1.0304 Top-1 准确率: 0.7403 Top-5 准确率: 0.9573\n",
      "val 损失: 0.9316 Top-1 准确率: 0.7415 Top-5 准确率: 0.9314\n",
      "\n",
      "第 7/29 轮训练\n",
      "----------\n",
      "train 损失: 1.0232 Top-1 准确率: 0.7413 Top-5 准确率: 0.9589\n",
      "val 损失: 0.9431 Top-1 准确率: 0.7390 Top-5 准确率: 0.9312\n",
      "\n",
      "第 8/29 轮训练\n",
      "----------\n",
      "train 损失: 1.0118 Top-1 准确率: 0.7435 Top-5 准确率: 0.9622\n",
      "val 损失: 0.9514 Top-1 准确率: 0.7346 Top-5 准确率: 0.9297\n",
      "\n",
      "第 9/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9993 Top-1 准确率: 0.7461 Top-5 准确率: 0.9625\n",
      "val 损失: 0.9311 Top-1 准确率: 0.7392 Top-5 准确率: 0.9334\n",
      "\n",
      "第 10/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9935 Top-1 准确率: 0.7474 Top-5 准确率: 0.9657\n",
      "val 损失: 0.9457 Top-1 准确率: 0.7367 Top-5 准确率: 0.9294\n",
      "\n",
      "第 11/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9811 Top-1 准确率: 0.7499 Top-5 准确率: 0.9657\n",
      "val 损失: 0.9429 Top-1 准确率: 0.7372 Top-5 准确率: 0.9304\n",
      "\n",
      "第 12/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9874 Top-1 准确率: 0.7489 Top-5 准确率: 0.9668\n",
      "val 损失: 0.9375 Top-1 准确率: 0.7439 Top-5 准确率: 0.9323\n",
      "\n",
      "第 13/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9681 Top-1 准确率: 0.7523 Top-5 准确率: 0.9688\n",
      "val 损失: 0.9350 Top-1 准确率: 0.7418 Top-5 准确率: 0.9332\n",
      "\n",
      "第 14/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9731 Top-1 准确率: 0.7530 Top-5 准确率: 0.9704\n",
      "val 损失: 0.9398 Top-1 准确率: 0.7392 Top-5 准确率: 0.9315\n",
      "\n",
      "第 15/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9541 Top-1 准确率: 0.7562 Top-5 准确率: 0.9704\n",
      "val 损失: 0.9491 Top-1 准确率: 0.7397 Top-5 准确率: 0.9303\n",
      "\n",
      "第 16/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9482 Top-1 准确率: 0.7579 Top-5 准确率: 0.9714\n",
      "val 损失: 0.9364 Top-1 准确率: 0.7401 Top-5 准确率: 0.9330\n",
      "\n",
      "第 17/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9548 Top-1 准确率: 0.7553 Top-5 准确率: 0.9713\n",
      "val 损失: 0.9413 Top-1 准确率: 0.7400 Top-5 准确率: 0.9312\n",
      "\n",
      "第 18/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9451 Top-1 准确率: 0.7571 Top-5 准确率: 0.9727\n",
      "val 损失: 0.9404 Top-1 准确率: 0.7448 Top-5 准确率: 0.9317\n",
      "\n",
      "第 19/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9337 Top-1 准确率: 0.7596 Top-5 准确率: 0.9743\n",
      "val 损失: 0.9464 Top-1 准确率: 0.7398 Top-5 准确率: 0.9316\n",
      "\n",
      "第 20/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9272 Top-1 准确率: 0.7601 Top-5 准确率: 0.9747\n",
      "val 损失: 0.9323 Top-1 准确率: 0.7448 Top-5 准确率: 0.9319\n",
      "\n",
      "第 21/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9212 Top-1 准确率: 0.7635 Top-5 准确率: 0.9758\n",
      "val 损失: 0.9436 Top-1 准确率: 0.7423 Top-5 准确率: 0.9307\n",
      "\n",
      "第 22/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9132 Top-1 准确率: 0.7651 Top-5 准确率: 0.9751\n",
      "val 损失: 0.9485 Top-1 准确率: 0.7412 Top-5 准确率: 0.9301\n",
      "\n",
      "第 23/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9181 Top-1 准确率: 0.7634 Top-5 准确率: 0.9770\n",
      "val 损失: 0.9560 Top-1 准确率: 0.7413 Top-5 准确率: 0.9301\n",
      "\n",
      "第 24/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9173 Top-1 准确率: 0.7639 Top-5 准确率: 0.9774\n",
      "val 损失: 0.9520 Top-1 准确率: 0.7436 Top-5 准确率: 0.9286\n",
      "\n",
      "第 25/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9008 Top-1 准确率: 0.7682 Top-5 准确率: 0.9785\n",
      "val 损失: 0.9431 Top-1 准确率: 0.7451 Top-5 准确率: 0.9317\n",
      "\n",
      "第 26/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9005 Top-1 准确率: 0.7669 Top-5 准确率: 0.9786\n",
      "val 损失: 0.9478 Top-1 准确率: 0.7414 Top-5 准确率: 0.9297\n",
      "\n",
      "第 27/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8948 Top-1 准确率: 0.7677 Top-5 准确率: 0.9796\n",
      "val 损失: 0.9459 Top-1 准确率: 0.7434 Top-5 准确率: 0.9299\n",
      "\n",
      "第 28/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8922 Top-1 准确率: 0.7686 Top-5 准确率: 0.9793\n",
      "val 损失: 0.9582 Top-1 准确率: 0.7416 Top-5 准确率: 0.9275\n",
      "\n",
      "第 29/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8834 Top-1 准确率: 0.7693 Top-5 准确率: 0.9808\n",
      "val 损失: 0.9583 Top-1 准确率: 0.7419 Top-5 准确率: 0.9278\n",
      "\n",
      "训练完成，耗时 27m 44s\n",
      "最佳验证集准确率: 0.745100\n"
     ]
    }
   ],
   "source": [
    "model2,(train_losses2, train_top1_accs2, train_top5_accs2, val_losses2, val_top1_accs2, val_top5_accs2) = train_model(model,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs = 30)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/9 轮训练\n",
      "----------\n",
      "train 损失: 0.9079 Top-1 准确率: 0.7648 Top-5 准确率: 0.9782\n",
      "val 损失: 0.9520 Top-1 准确率: 0.7421 Top-5 准确率: 0.9301\n",
      "\n",
      "第 1/9 轮训练\n",
      "----------\n",
      "train 损失: 0.9046 Top-1 准确率: 0.7645 Top-5 准确率: 0.9792\n",
      "val 损失: 0.9495 Top-1 准确率: 0.7420 Top-5 准确率: 0.9312\n",
      "\n",
      "第 2/9 轮训练\n",
      "----------\n",
      "train 损失: 0.9012 Top-1 准确率: 0.7656 Top-5 准确率: 0.9799\n",
      "val 损失: 0.9524 Top-1 准确率: 0.7455 Top-5 准确率: 0.9300\n",
      "\n",
      "第 3/9 轮训练\n",
      "----------\n",
      "train 损失: 0.8816 Top-1 准确率: 0.7702 Top-5 准确率: 0.9816\n",
      "val 损失: 0.9631 Top-1 准确率: 0.7396 Top-5 准确率: 0.9286\n",
      "\n",
      "第 4/9 轮训练\n",
      "----------\n",
      "train 损失: 0.8854 Top-1 准确率: 0.7701 Top-5 准确率: 0.9811\n",
      "val 损失: 0.9390 Top-1 准确率: 0.7472 Top-5 准确率: 0.9301\n",
      "\n",
      "第 5/9 轮训练\n",
      "----------\n",
      "train 损失: 0.8816 Top-1 准确率: 0.7706 Top-5 准确率: 0.9816\n",
      "val 损失: 0.9688 Top-1 准确率: 0.7421 Top-5 准确率: 0.9260\n",
      "\n",
      "第 6/9 轮训练\n",
      "----------\n",
      "train 损失: 0.8674 Top-1 准确率: 0.7739 Top-5 准确率: 0.9818\n",
      "val 损失: 0.9730 Top-1 准确率: 0.7416 Top-5 准确率: 0.9268\n",
      "\n",
      "第 7/9 轮训练\n",
      "----------\n",
      "train 损失: 0.8748 Top-1 准确率: 0.7714 Top-5 准确率: 0.9827\n",
      "val 损失: 0.9683 Top-1 准确率: 0.7408 Top-5 准确率: 0.9271\n",
      "\n",
      "第 8/9 轮训练\n",
      "----------\n",
      "train 损失: 0.8806 Top-1 准确率: 0.7697 Top-5 准确率: 0.9844\n",
      "val 损失: 0.9570 Top-1 准确率: 0.7439 Top-5 准确率: 0.9280\n",
      "\n",
      "第 9/9 轮训练\n",
      "----------\n",
      "train 损失: 0.8629 Top-1 准确率: 0.7742 Top-5 准确率: 0.9832\n",
      "val 损失: 0.9577 Top-1 准确率: 0.7431 Top-5 准确率: 0.9280\n",
      "\n",
      "训练完成，耗时 9m 13s\n",
      "最佳验证集准确率: 0.747200\n"
     ]
    }
   ],
   "source": [
    "model3,(train_losses3, train_top1_accs3, train_top5_accs3, val_losses3, val_top1_accs3, val_top5_accs3) = train_model(model2,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs = 10)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model3, '90epoch_highway+02drop.pth')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里的隐藏层是1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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